I work in a filed were there are many publications based on classifiers trained on small samples sizes (but large amount of features). In most cases the sample size can only be increased by a few instances based on the cumbersome annotation process.
I built a classifier for a multi-classification problem, that has a micro-F1 score of 0.8 based on a cross validation performed on approx. 2000 samples. There is a published study, where they also reached an micro-F1 score of 0.8, but their sample size was approx. 1000 samples.
Unfortunately the identical 1000 sample data set can not be used (it is nor public). Can I still make an argument, that my classifier might perform better on new data, since the training size was twice as much ?
Are there any studies available were performance is compared with sample size for small sample classification.